SoroushVahidi/scidocs huggingface dataset:
Consensus in the Age of Blockchains
(2017)

The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
What influences literacy outcome in children with speech sound disorder?
(2009)

PURPOSE
In this study, the authors evaluated literacy outcome in children with histories of speech sound disorder (SSD) who were characterized along 2 dimensions: broader language function and persistence of SSD. In previous studies, authors have demonstrated that each dimension relates to literacy but have not disentangled their effects. Methods Two groups of children (86 SSD and 37 controls) were recruited at ages 5-6 and were followed longitudinally. The authors report the literacy of children with SSD at ages 7-9, compared with controls and national norms, and relative to language skill and SSD persistence (both measured at age 5-6).


RESULTS
The SSD group demonstrated elevated rates of reading disability. Language skill but not SSD persistence predicted later literacy. However, SSD persistence was associated with phonological awareness impairments. Phonological awareness alone predicted literacy outcome less well than a model that also included syntax and nonverbal IQ.


CONCLUSIONS
Results support previous literature findings that SSD history predicts literacy difficulties and that the association is strongest for SSD + language impairment (LI). Magnitude of phonological impairment alone did not determine literacy outcome, as predicted by the core phonological deficit hypothesis. Instead, consistent with a multiple deficit approach, phonological deficits appeared to interact with other cognitive factors in literacy development.
Low-Cost 60-GHz Smart Antenna Receiver Subsystem Based on Substrate Integrated Waveguide Technology
(2012)

In this paper, a low-cost integrated 60-GHz switched-beam smart antenna subsystem is studied and demonstrated experimentally for the first time based on almost all 60-GHz substrate integrated waveguide (SIW) components including a slot antenna, 4×4 Butler matrix network, bandpass filter, sub-harmonically pumped mixer, and local oscillator (LO) source. In this study, an antenna array, a Butler matrix, and a bandpass filter are integrated and fabricated into one single substrate. Instead of using a 60-GHz LO source, a 30-GHz LO source is developed to drive a low-cost 60-GHz sub-harmonically pumped mixer. This 30-GHz LO circuit consists of 10-GHz SIW voltage-controlled oscillator and frequency tripler. Following the frequency down-conversion of four 60-GHz signals coming from the 4×4 Butler matrix and a comparison of the four IF signals executed in the digital processor based on the maximum received power criterion, control signals will be feed-backed to drive the single-pole four-throw switch array and then the beam is tuned in order to point toward the main beam of the transmit antenna. In this way, the arriving 60-GHz RF signal can be tracked effectively. All designed components are verified experimentally. The proposed smart receiver subsystem that integrates all those front-end components is concluded with satisfactory measured results.
Quarter-Mode Substrate Integrated Waveguide and Its Application to Antennas Design
(2013)

A quarter-mode substrate integrated waveguide (QMSIW) which is the quadrant sector of a square waveguide resonator is proposed and investigated in this paper. The QMSIW is realized by bisecting the half-mode substrate integrated waveguide (HMSIW) into two parts along the fictitious quasi-magnetic wall when it operates with TE101 and TE202 modes as the way bisecting the substrate integrated waveguide (SIW) to HMSIW. The QMSIW can almost preserve the field distribution of original SIW and leaky wave is achieved from the dielectric aperture of the QMSIW. When the feeding port is placed at one corner of the QMSIW, a linearly polarized radiation is obtained when the QMSIW resonates in TE101QM mode, and when the QMSIW resonates in TE202QM mode, a circularly polarized (CP) wave is achieved. An antenna is designed, fabricated, and measured based on the proposed QMSIW. The measurement results match with the simulation results very well.
LTCC Packages With Embedded Phased-Array Antennas for 60 GHz Communications
(2011)

A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.
Enhancing the quality of life through wearable technology
(2003)

An overview of the key challenges facing the practice of medicine today is presented along with the need for technological solutions that can "prevent" problems. Then, the development of the Wearable Motherboard/spl trade/ (Smart Shirt) as a platform for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life-from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare - all aimed at enhancing the quality of life for humans - are presented.
Automatic fine tuning of cavity filters Automatisk finjustering av kavitetsfilter
(2016)

Cavity filters are a necessary component in base stations used for telecommunication. Without these filters it would not be possible for base stations to send and receive signals at the same time. Today these cavity filters require fine tuning by humans before they can be deployed. This thesis have designed and implemented a neural network that can tune cavity filters. Different types of design parameters have been evaluated, such as neural network architecture, data presentation and data preprocessing. While the results was not comparable to human fine tuning, it was shown that there was a relationship between error and number of weights in the neural network. The thesis also presents some rules of thumb for future designs of neural network used for filter tuning.
CACHE MISSING FOR FUN AND PROFIT
(2005)

Simultaneous multithreading — put simply, the sharing of the execution resources of a superscalar processor between multiple execution threads — has recently become widespread via its introduction (under the name “Hyper-Threading”) into Intel Pentium 4 processors. In this implementation, for reasons of efficiency and economy of processor area, the sharing of processor resources between threads extends beyond the execution units; of particular concern is that the threads share access to the memory caches. We demonstrate that this shared access to memory caches provides not only an easily used high bandwidth covert channel between threads, but also permits a malicious thread (operating, in theory, with limited privileges) to monitor the execution of another thread, allowing in many cases for theft of cryptographic keys. Finally, we provide some suggestions to processor designers, operating system vendors, and the authors of cryptographic software, of how this attack could be mitigated or eliminated entirely.
Grounded Theory and the Constant Comparative Method : Valid Research Strategies for Educators
(2012)

University of Wisconsin-Whitewater. USA ___________________________________________________________________________ Grounded theory was developed by Glaser and Strauss who believed that theory could emerge through qualitative data analysis. In grounded theory, the researcher uses multiple stages of collecting, refining, and categorizing the data. The researcher uses the strategies of the making constant comparisons and applying theoretical sampling to obtain a theory grounded in the data. The justification of this paper is to provide discussion on the validity of grounded theory and the constant comparative method as effective research strategies for educators. The qualitative design of grounded theory will be the focus of this paper, along with a discussion of the constant comparative method, issues related to trustworthiness, and limitations inherent in grounded theory methodology __________________________________________________________________________________________
High performance CAN/FlexRay gateway design for in-vehicle network
(2017)

A gateway in in-vehicle network (IVN) is an indispensable component as a conversion device between different communication protocols. Due to meet more complex network for IVN, how to improving the performance of gateway is more important. In this paper, a high efficient of CAN/FlexRay gateway using hardware/software (HW/SW) co-design method is proposed which is implemented with Xilinx FPGA system. The CAN controller and FlexRay controller are designed by SW and HW, respectively. The interface of HW and SW are used AXI-bus. The convert module is evaluated by a cost function. In addition, a transceiver PCB for connecting between the proposed gateway and external CAN/FlexRay-based ECUs is implemented. Experimental results show that the proposed gateway system can reduce at most 94.7% on execution time with related works which is more suited for IVN.
Mobile-assisted localization in wireless sensor networks
(2005)

The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a "globally rigid'* structure that guarantees a unique localization. We derive the required constraints on the mobile's movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile's movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL's median pairwise distance error is less than 1.5% of the true node distance.
Twitter as an instrument for crisis response: The Typhoon Haiyan case study
(2015)

The research presented in this paper attempts an initial evaluation of Twitter as an instrument for emergency response in the context of a recent crisis event. The case of the 2013 disaster, when typhoon Haiyan hit Philippines is examined by analyzing nine consecutive days of Twitter messages and comparing them to the actual events. The results indicate that during disasters, Twitter users tend to post messages to enhance situation awareness and to motivate people to act. Furthermore, tweets were found reliable and provided valuable information content, supporting the argument that Twitter presents a very good potential to become a useful tool in situations where rapid emergency response is essential.
Analysis and modelling of broad-band ferrite-based coaxial transmission-line transformers
(2010)

The work presented in the paper focuses on accuracy of models for broad-band ferrite based coaxial transmission-line transformers. Soft-ferrites are largely used in VHF/UHF components allowing band enlargement on the low-edge side. Degradation of frequency performance on the high-edge side are produced both by ferrite losses, and by parasitic capacitance due to connection to the thermal and electrical ground in high power applications. Both a circuital model for low-power applications and a scalable e.m. model for high-power applications are presented and discussed.
Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words
(1994)

In this paper we propose the use of WordNet as a knowledge base in an information retrieval task. The application areas range from information filtering and document retrieval to multimedia retrieval and data sharing in large scale distributed database systems. The WordNet derived knowledge base makes semantic knowledge available which can be used in overcoming many problems associated with the richness of natural language. A semantic similarity measure is also proposed which can be used as an alternative to pattern matching in the comparison process.
Automatic Labelling of Topic Models
(2011)

We propose a method for automatically labelling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates using a combination of association measures and lexical features, optionally fed into a supervised ranking model. Our method is shown to perform strongly over four independent sets of topics, significantly better than a benchmark method.
Deep discriminative manifold learning
(2016)

This paper presents a new non-linear dimensionality reduction with stochastic neighbor embedding. A deep neural network is developed for discriminative manifold learning where the class information in transformed low-dimensional space is preserved. Importantly, the objective function for deep manifold learning is formed as the Kullback-Leibler divergence between the probability measures of the labeled samples in high-dimensional and low-dimensional spaces. Different from conventional methods, the derived objective does not require the empirically-tuned parameter. This objective is optimized to attractive those samples from the same class to be close together and simultaneously impose those samples from different classes to be far apart. In the experiments on image and audio tasks, we illustrate the effectiveness of the proposed discriminative manifold learning in terms of visualization and classification performance.
Adaptive Nonlinear Model Inversion Control of a Twin Rotor System Using Artificial Intelligence
(2007)

The paper investigates the development of an adaptive dynamic nonlinear model inversion control law for a twin rotor MIMO system (TRMS) utilizing artificial neural networks and genetic algorithms. The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel. In the absence of model inversion errors, a genetic algorithm-tuned PD controller is used to enhance the tracking characteristics of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. In order to show the effectiveness of the proposed method in the simulation environment an inversion error has deliberately been provided as an uncertainty in the real situation. Square and sinusoidal reference command signals are used to test the control system performance, and it is noted that an excellent tracking response is exhibited in the presence of inversion errors caused by model uncertainty.
Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation
(2018)

Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
(2017)

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
Operating Systems for Low-End Devices in the Internet of Things: A Survey
(2016)

The Internet of Things (IoT) is projected to soon interconnect tens of billions of new devices, in large part also connected to the Internet. IoT devices include both high-end devices which can use traditional go-to operating systems (OSs) such as Linux, and low-end devices which cannot, due to stringent resource constraints, e.g., very limited memory, computational power, and power supply. However, large-scale IoT software development, deployment, and maintenance requires an appropriate OS to build upon. In this paper, we thus analyze in detail the specific requirements that an OS should satisfy to run on low-end IoT devices, and we survey applicable OSs, focusing on candidates that could become an equivalent of Linux for such devices, i.e., a one-size-fits-most, open source OS for low-end IoT devices.
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
(2003)

We propose a content-based soft annotation (CBSA) procedure for providing images with semantical labels. The annotation procedure starts with labeling a small set of training images, each with one single semantical label (e.g., forest, animal, or sky). An ensemble of binary classifiers is then trained for predicting label membership for images. The trained ensemble is applied to each individual image to give the image multiple soft labels, and each label is associated with a label membership factor. To select a base binary-classifier for CBSA, we experiment with two learning methods, Support Vector Machines (SVMs) and Bayes Point Machines (BPMs, and compare their class-prediction accuracy. Our empirical study on a 116-category 25K-image set shows that the BPM-based ensemble provides better annotation quality than the SVM-based ensemble for supporting multimodal image retrievals.
0.293-mm2 Fast Transient Response Hysteretic Quasi- $V^{2}$  DC–DC Converter With Area-Efficient Time-Domain-Based Controller in 0.35- $\mu$ m CMOS
(2018)

A time-domain-control-based quasi-<inline-formula> <tex-math notation="LaTeX">$V^{2}$ </tex-math></inline-formula> buck converter with small area, fast transient response, wide load current range, and constant switching frequency is proposed in this paper. The proposed time-domain-based controller achieves compact core area and simplified constant switching frequency control by replacing the conventional voltage-domain comparator with a phase detector having an adaptive detection window. Moreover, a coupling-based inductor current emulator is also proposed to support the discontinuous conduction mode operation in the light load. The proposed buck converter is implemented using 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The core area of the proposed buck converter is only 0.293 mm<sup>2</sup>. The measured peak efficiency in the continuous conduction mode is 92% and the end-to-end efficiency in the load current range of 25–700 mA is 73%. The undershoot/overshoot and recovery time with the up/down load current steps of 510 mA are 38/20 mV and 2.5/2.6 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively.